Medical imaging techniques provide doctors and medical technicians with valuable data for patient diagnosis and care. Various imaging techniques include cardiac angiography, peripheral angiography, radiography, computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI). All of these imaging techniques produce medical images that are then studied by medical personnel in making diagnoses. When these imaging techniques produce images, the images have a dataset of pixels or voxels that can be manipulated to increase image quality. It will be appreciated that higher image quality will lead to a more accurate diagnosis. The images produced by the above-listed techniques can be rendered by maximum intensity projection (MIP), which is a widely used volumetric rendering technique for medical diagnostic imaging. MIP is commonly used to extract vascular structures from medical CT or MRI data sets, such as with angiography.
With MIP, at each pixel the highest sample value encountered along the corresponding viewing ray is determined. There are several MIP rendering methods, including Shearwarp, Splatting, and Raycast based methods. Among these models, the Ray Cast based methods produce the best image quality. With Ray Cast methods, for every pixel in the output rendered image, a ray is shot into the volume at a certain viewing direction. This ray is sampled along the volume at constant intervals, which are determined from the zoom factor. The maximum sample value along the ray is then calculated and stored in the rendered image pixel. This process is repeated until all the rays from every pixel in the output image are cast and the maximum along each of them is calculated.
In order to sample the volume along each ray, a suitable interpolation method, such as a tri-linear interpolation method, is often used. The ti-linear interpolation method makes use of linearly weighted averages of eight neighboring voxels to calculate the intensity of a sample. A voxel (“volumetric pixel” or 3-D pixel) is a volume element, representing a value on a regular grid in three dimensional space. Voxels are analogous to pixels, which represent 2-D image data. Voxels are frequently used in the visualization and analysis of medical and scientific data. Voxels are the smallest distinguishable box-shaped part of a 3-D image, which is obtained by multiplying the pixel area by the slice thickness. The weight of each neighboring voxel is determined by its distance from the sample value. The closer the neighboring voxel is to the sample, the greater its weight.
Due to the presence of wide band noise in the volume data, the maximum sample values of rays that lie near or on the voxel grid are likely to be influenced by the noise and thus will have a higher intensity value as compared to those rays that lie in the middle of the voxel grids. This difference results in the formation of stripes in the MIP images. The width of the stripe depends on the zoom factor or the ratio of voxel distance and the pixel distance. The stripes can be formed both horizontally and vertically, which results in the chessboard like pattern. Since the noise is wideband in nature, there is no effective method to pr-filter the volume data set without damaging the signal components and lowering MIP image resolution.
A technique for eliminating chessboard artifacts and/or stripe like artifacts is described in detail in pending U.S. patent application Ser. No. 11/771,329, filed Jun. 29, 2007 by Smita Krishnan et al., entitled “Systems and Methods of Image Rendering from Datasets,” the entirety of which application is incorporated herein by reference. This chessboard mitigation method introduces certain localized high frequency loss. Thus, in order to maintain a desired high fidelity of a MIP image the mitigation method should only be applied when the stripe or chessboard artifacts in the regular MIP image exist.
Currently artifact detection is performed purely by visual examination. However, this requires an end user interaction, which does not fit well into clinical workflow. Thus, it would be desirable to provide a method to automatically detect whether stripe-like chessboard patterns exist in the rendered MIP images.